Prevalent speaker recognition methods use only spectral-envelope based features such as MFCC, ignoring the rich speaker identity information contained in the temporal- spectral dynamics of the entire speech signal. We propose a new feature for speaker recognition called compressed spectral dynamics (CSD) which effectively captures such spectral dynamics and the inherent speaker identity. The discriminative power of CSD allows the classification part to remain simple. The proposed method, a simple nearest neighbor classifier using CSD, delivers performance competitive to conventional MFCC+DTW based text-dependent speaker recognition methods at significantly reduced complexity.
Cite as: Das, A., Chittaranjan, G. (2008) Automated speaker recognition using compressed temporal-spectral dynamics information of password spectrograms. Proc. ISCA ITRW on Speech Analysis and Processing for Knowledge Discovery, paper 014
@inproceedings{das08_spkd,
author={Amitava Das and Gokul Chittaranjan},
title={{Automated speaker recognition using compressed temporal-spectral dynamics information of password spectrograms}},
year=2008,
booktitle={Proc. ISCA ITRW on Speech Analysis and Processing for Knowledge Discovery},
pages={paper 014}
}